Abstract

Longitudinal data collected as part of usual healthcare delivery are becoming increasingly available for research through electronic health records. However, a common feature of these data is that they are collected more frequently when patients are unwell. For example, newborns who are slow to regain their birthweight will require more frequent monitoring and will consequently have more weight measurements than their typically growing counterparts. Failing to account for this would lead to underestimation of the rate of growth of the population of newborns as a whole. I will discuss approaches to handling the informative nature of the observation, including recent developments to handle other data complexities such as clustering, causal inference, and variable selection.